1 citations · 1 across the 2 of their papers we have counts for
5 papers
MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale
Bin Wang, Tianyao He, Linke Ouyang +40
Current document parsing methods advance primarily through model architecture innovation, while systematic engineering of training data remains underexplored. Yet state-of-the-art…
TRivia: Self-supervised Fine-tuning of Vision-Language Models for Table Recognition
Junyuan Zhang, Bin Wang, Qintong Zhang +13
Table recognition (TR) aims to transform table images into semi-structured representations such as HTML or Markdown. As a core component of document parsing, TR has long relied on…
AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generation
Zhengren Wang, Dongsheng Ma, Huaping Zhong +4
The expansion of retrieval-augmented generation (RAG) into multimodal domains has intensified the challenge for processing complex visual documents, such as financial reports. Whil…
Not All Documents Are What You Need for Extracting Instruction Tuning Data
Chi Zhang, Huaping Zhong, Hongtao Li +11
Instruction tuning improves the performance of large language models (LLMs), but it heavily relies on high-quality training data. Recently, LLMs have been used to synthesize instru…
Harnessing Diversity for Important Data Selection in Pretraining Large Language Models
Chi Zhang, Huaping Zhong, Kuan Zhang +10
Data selection is of great significance in pre-training large language models, given the variation in quality within the large-scale available training corpora. To achieve this, re…